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yzhao062 committed Nov 28, 2018
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Expand Up @@ -63,7 +63,7 @@ PyOD is a comprehensive and scalable **Python toolkit** for **detecting outlying
multivariate data. This exciting yet challenging field is commonly referred as
`Outlier Detection <https://en.wikipedia.org/wiki/Anomaly_detection>`_
or `Anomaly Detection <https://en.wikipedia.org/wiki/Anomaly_detection>`_.
Since 2017, PyOD has been successfully used in various academic researches [4, 8, 17] and commercial products.
Since 2017, PyOD has been successfully used in various academic researches [#Zhao2018DCSO]_ [#Zhao2018XGBOD]_ [#Zhao2019LSCP]_ and commercial products.
PyOD is featured for:


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^^^^^^^^^^^^^^^^^^^^^^^


* `View the latest codes on Github <https://github.com/yzhao062/Pyod>`_
* `View the latest codes on Github <https://github.com/yzhao062/pyod>`_
* `Execute Interactive Jupyter Notebooks <https://mybinder.org/v2/gh/yzhao062/pyod/master>`_
* `Anomaly Detection Resources <https://github.com/yzhao062/anomaly-detection-resources>`_

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#. Probabilistic Models for Outlier Detection:

#. **ABOD: Angle-Based Outlier Detection** [7]
#. **FastABOD: Fast Angle-Based Outlier Detection using approximation** [7]
#. **ABOD: Angle-Based Outlier Detection** [#Kriegel2008Angle]_
#. **FastABOD: Fast Angle-Based Outlier Detection using approximation** [#Kriegel2008Angle]_

#. Outlier Ensembles and Combination Frameworks

#. **Isolation Forest** [2]
#. **Feature Bagging** [9]
#. **Isolation Forest** [#Liu2008Isolation]_
#. **Feature Bagging** [#Lazarevic2005Feature]_

#. Neural Networks and Deep Learning Models (implemented in Keras)

#. **AutoEncoder with Fully Connected NN** [16, Chapter 3]
#. **AutoEncoder with Fully Connected NN** [#Aggarwal2015Outlier]_ [Chapter 3]

FAQ regarding AutoEncoder in PyOD and debugging advice:
`known issues <https://github.com/yzhao062/Pyod/issues/19>`_

**Outlier Detector/Scores Combination Frameworks**:

#. **Feature Bagging**\ : build various detectors on random selected features [9]
#. **Average** & **Weighted Average**\ : simply combine scores by averaging [6]
#. **Feature Bagging**\ : build various detectors on random selected features [#Lazarevic2005Feature]_
#. **Average** & **Weighted Average**\ : simply combine scores by averaging [#Aggarwal2015Theoretical]_
#. **Maximization**\ : simply combine scores by taking the maximum across all
base detectors [6]
#. **Average of Maximum (AOM)** [6]
#. **Maximum of Average (MOA)** [6]
#. **Threshold Sum (Thresh)** [6]
base detectors [#Aggarwal2015Theoretical]_
#. **Average of Maximum (AOM)** [#Aggarwal2015Theoretical]_
#. **Maximum of Average (MOA)** [#Aggarwal2015Theoretical]_
#. **Threshold Sum (Thresh)** [#Aggarwal2015Theoretical]_

**Comparison of all implemented models** are made available below:
(\ `Figure <https://raw.githubusercontent.com/yzhao062/Pyod/master/examples/ALL.png>`_\ ,
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^^^^^^^^^


.. [#Aggarwal2015Outlier] Aggarwal, C.C., 2015. Outlier analysis. In Data mining (pp. 237-263). Springer, Cham.
[4] Y. Zhao and M.K. Hryniewicki, "DCSO: Dynamic Combination of Detector Scores for Outlier Ensembles," *ACM SIGKDD Workshop on Outlier Detection De-constructed (ODD v5.0)*\ , 2018.
.. [#Aggarwal2015Theoretical] Aggarwal, C.C. and Sathe, S., 2015. Theoretical foundations and algorithms for outlier ensembles.\ *ACM SIGKDD Explorations Newsletter*\ , 17(1), pp.24-47.
.. [#Goldstein2012Histogram] Goldstein, M. and Dengel, A., 2012. Histogram-based outlier score (hbos): A fast unsupervised anomaly detection algorithm. In *KI-2012: Poster and Demo Track*\ , pp.59-63.
[6] Aggarwal, C.C. and Sathe, S., 2015. Theoretical foundations and algorithms for outlier ensembles.\ *ACM SIGKDD Explorations Newsletter*\ , 17(1), pp.24-47.

[7] Kriegel, H.P. and Zimek, A., 2008, August. Angle-based outlier detection in high-dimensional data. In *KDD '08*\ , pp. 444-452. ACM.

[8] Y. Zhao and M.K. Hryniewicki, "XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning," *IEEE International Joint Conference on Neural Networks*\ , 2018.
.. [#Angiulli2002Fast] Angiulli, F. and Pizzuti, C., 2002, August. Fast outlier detection in high dimensional spaces. In *European Conference on Principles of Data Mining and Knowledge Discovery* pp. 15-27.
[9] Lazarevic, A. and Kumar, V., 2005, August. Feature bagging for outlier detection. In *KDD '05*. 2005.
.. [#Breunig2000LOF] Breunig, M.M., Kriegel, H.P., Ng, R.T. and Sander, J., 2000, May. LOF: identifying density-based local outliers. *ACM Sigmod Record*\ , 29(2), pp. 93-104.
.. [#Shyu2003A] Shyu, M.L., Chen, S.C., Sarinnapakorn, K. and Chang, L., 2003. A novel anomaly detection scheme based on principal component classifier. *MIAMI UNIV CORAL GABLES FL DEPT OF ELECTRICAL AND COMPUTER ENGINEERING*.
.. [#Goldstein2012Histogram] Goldstein, M. and Dengel, A., 2012. Histogram-based outlier score (hbos): A fast unsupervised anomaly detection algorithm. In *KI-2012: Poster and Demo Track*\ , pp.59-63.
.. [#Hardin2004Outlier] Hardin, J. and Rocke, D.M., 2004. Outlier detection in the multiple cluster setting using the minimum covariance determinant estimator. *Computational Statistics & Data Analysis*\ , 44(4), pp.625-638.
.. [#He2003Discovering] He, Z., Xu, X. and Deng, S., 2003. Discovering cluster-based local outliers. *Pattern Recognition Letters*\ , 24(9-10), pp.1641-1650.
[16] Aggarwal, C.C., 2015. Outlier analysis. In Data mining (pp. 237-263). Springer, Cham.
.. [#Kriegel2008Angle] Kriegel, H.P. and Zimek, A., 2008, August. Angle-based outlier detection in high-dimensional data. In *KDD '08*\ , pp. 444-452. ACM.
[17] Zhao, Y., Hryniewicki, M.K., Nasrullah, Z., and Li, Z. SCP: Selective Combination in Parallel Outlier Ensembles. *SIAM International Conference on Data Mining (SDM)*. **Currently Under Review**.


----

.. [#Angiulli2002Fast] Angiulli, F. and Pizzuti, C., 2002, August. Fast outlier detection in high dimensional spaces. In *European Conference on Principles of Data Mining and Knowledge Discovery* pp. 15-27.
.. [#Breunig2000LOF] Breunig, M.M., Kriegel, H.P., Ng, R.T. and Sander, J., 2000, May. LOF: identifying density-based local outliers. *ACM Sigmod Record*\ , 29(2), pp. 93-104.
.. [#Lazarevic2005Feature] Lazarevic, A. and Kumar, V., 2005, August. Feature bagging for outlier detection. In *KDD '05*. 2005.
.. [#Liu2008Isolation] Liu, F.T., Ting, K.M. and Zhou, Z.H., 2008, December. Isolation forest. In *International Conference on Data Mining*\ , pp. 413-422. IEEE.
Expand All @@ -497,4 +486,10 @@ Reference
.. [#Rousseeuw1999A] Rousseeuw, P.J. and Driessen, K.V., 1999. A fast algorithm for the minimum covariance determinant estimator. *Technometrics*\ , 41(3), pp.212-223.
.. [#Hardin2004Outlier] Hardin, J. and Rocke, D.M., 2004. Outlier detection in the multiple cluster setting using the minimum covariance determinant estimator. *Computational Statistics & Data Analysis*\ , 44(4), pp.625-638.
.. [#Shyu2003A] Shyu, M.L., Chen, S.C., Sarinnapakorn, K. and Chang, L., 2003. A novel anomaly detection scheme based on principal component classifier. *MIAMI UNIV CORAL GABLES FL DEPT OF ELECTRICAL AND COMPUTER ENGINEERING*.
.. [#Zhao2018DCSO] Zhao, Y. and Hryniewicki, M.K. DCSO: Dynamic Combination of Detector Scores for Outlier Ensembles. *ACM SIGKDD Workshop on Outlier Detection De-constructed (ODD v5.0)*\ , 2018.
.. [#Zhao2018XGBOD] Zhao, Y. and Hryniewicki, M.K. XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning. *IEEE International Joint Conference on Neural Networks*\ , 2018.
.. [#Zhao2019LSCP] Zhao, Y., Hryniewicki, M.K., Nasrullah, Z., and Li, Z. LSCP: Locally Selective Combination of Parallel Outlier Ensembles. *SIAM International Conference on Data Mining (SDM)*. **Currently Under Review**.

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